{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "befffeef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2025-01-01', '2025-01-02', '2025-01-03', '2025-01-04',\n",
      "               '2025-01-05', '2025-01-06'],\n",
      "              dtype='datetime64[ns]', freq='D')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from IPython.display import display\n",
    "\n",
    "s = pd.Series([1, 3, 6, np.nan, 44, 1])  # np.nan => NaN\n",
    "\n",
    "dates = pd.date_range('20250101', periods=6)\n",
    "print(dates)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8906ec5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   a         b         c         d\n",
      "2025-01-01 -0.349219 -0.624424 -1.664858 -0.253767\n",
      "2025-01-02 -0.696312  0.341211 -0.090838 -0.168976\n",
      "2025-01-03  0.979610  0.251529  1.871574  0.445488\n",
      "2025-01-04  1.256284  0.233002 -0.089660 -0.346225\n",
      "2025-01-05  0.924814 -0.595288 -0.340565 -1.628973\n",
      "2025-01-06  0.309242 -1.345683  1.221391 -0.437345\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=[\"a\", \"b\", \"c\", \"d\"])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "35a27a81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0  1   2   3\n",
      "0  0  1   2   3\n",
      "1  4  5   6   7\n",
      "2  8  9  10  11\n"
     ]
    }
   ],
   "source": [
    "df1 = pd.DataFrame(np.arange(12).reshape((3, 4)))\n",
    "print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e27b8129",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A          B    C  D      E    F\n",
      "0  1.0 2013-01-02  1.0  3   test  foo\n",
      "1  1.0 2013-01-02  1.0  3  train  foo\n",
      "2  1.0 2013-01-02  1.0  3   test  foo\n",
      "3  1.0 2013-01-02  1.0  3  train  foo\n",
      "--------------------\n",
      "Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object')\n",
      "[[1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'test' 'foo']\n",
      " [1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'train' 'foo']\n",
      " [1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'test' 'foo']\n",
      " [1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'train' 'foo']]\n",
      "\n",
      "\n",
      "                     0                    1                    2  \\\n",
      "A                  1.0                  1.0                  1.0   \n",
      "B  2013-01-02 00:00:00  2013-01-02 00:00:00  2013-01-02 00:00:00   \n",
      "C                  1.0                  1.0                  1.0   \n",
      "D                    3                    3                    3   \n",
      "E                 test                train                 test   \n",
      "F                  foo                  foo                  foo   \n",
      "\n",
      "                     3  \n",
      "A                  1.0  \n",
      "B  2013-01-02 00:00:00  \n",
      "C                  1.0  \n",
      "D                    3  \n",
      "E                train  \n",
      "F                  foo  \n",
      "\n",
      "\n",
      "     F      E  D    C          B    A\n",
      "0  foo   test  3  1.0 2013-01-02  1.0\n",
      "1  foo  train  3  1.0 2013-01-02  1.0\n",
      "2  foo   test  3  1.0 2013-01-02  1.0\n",
      "3  foo  train  3  1.0 2013-01-02  1.0\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'A': 1.,\n",
    "                   'B': pd.Timestamp('20130102'),\n",
    "                   'C': pd.Series(1, index=list(range(4)), dtype='float32'),\n",
    "                   'D': np.array([3] * 4, dtype='int32'),\n",
    "                   'E': pd.Categorical([\"test\", \"train\", \"test\", \"train\"]),\n",
    "                   'F': 'foo'\n",
    "                   })\n",
    "print(df2)\n",
    "print(\"-\" * 20)\n",
    "print(df2.columns)\n",
    "print(df2.values)\n",
    "print(\"\\n\")\n",
    "print(np.transpose(df2))\n",
    "print(\"\\n\")\n",
    "print(df2.sort_index(axis=1, ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dc5481cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             A   B   C   D\n",
      "2025-01-01   0   1   2   3\n",
      "2025-01-02   4   5   6   7\n",
      "2025-01-03   8   9  10  11\n",
      "2025-01-04  12  13  14  15\n",
      "2025-01-05  16  17  18  19\n",
      "2025-01-06  20  21  22  23\n"
     ]
    }
   ],
   "source": [
    "dates = pd.date_range(\"2025.01.01\", periods=6)\n",
    "df3 = pd.DataFrame(np.arange(24).reshape((6, 4)), index=dates, columns=['A', 'B', 'C', 'D'])\n",
    "print(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "757053f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             A   C\n",
      "2025-01-01   0   2\n",
      "2025-01-02   4   6\n",
      "2025-01-03   8  10\n",
      "2025-01-04  12  14\n",
      "2025-01-05  16  18\n",
      "2025-01-06  20  22\n"
     ]
    }
   ],
   "source": [
    "print(df3.loc[:, ['A', 'C']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba0503a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A    4\n",
      "B    5\n",
      "C    6\n",
      "D    7\n",
      "Name: 2025-01-02 00:00:00, dtype: int32\n",
      "------------------------------\n",
      "             B   C\n",
      "2025-01-04  13  14\n",
      "2025-01-05  17  18\n",
      "------------------------------\n",
      "            A  B   C   D\n",
      "2025-01-01  0  1   2   3\n",
      "2025-01-02  4  5   6   7\n",
      "2025-01-03  8  9  10  11\n",
      "------------------------------\n",
      "             A   B   C   D\n",
      "2025-01-05  16  17  18  19\n",
      "2025-01-06  20  21  22  23\n"
     ]
    }
   ],
   "source": [
    "print(df3.loc[\"2025.01.02\", :])  # loc base on label to select object\n",
    "print(\"-\" * 30)\n",
    "print(df3.iloc[3:5, 1:3])  # iloc base on position to select object\n",
    "print(\"-\" * 30)\n",
    "print(df3[0:3])\n",
    "print(\"-\" * 30)\n",
    "print(df3[(df3.A > 8) & (df3.B > 14)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c648023",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             A   B   C   D\n",
      "2025-01-01   0   1   2   3\n",
      "2025-01-02   4   5   6   7\n",
      "2025-01-03   8   9  10  11\n",
      "2025-01-04  12  13  14  15\n",
      "2025-01-05  16  17  13  19\n",
      "2025-01-06  20  21  22  23\n"
     ]
    }
   ],
   "source": [
    "df3.loc[\"2025.01.05\", 'C'] = 13\n",
    "print(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "edd7b231",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             A   B     C     D\n",
      "2025-01-01   0   1   2.0   3.0\n",
      "2025-01-02   4   5   6.0   NaN\n",
      "2025-01-03   8   9   NaN  11.0\n",
      "2025-01-04  12  13  14.0  15.0\n",
      "2025-01-05  16  17  13.0  19.0\n",
      "2025-01-06  20  21  22.0  23.0\n"
     ]
    }
   ],
   "source": [
    "# insert some NaN\n",
    "df3.iloc[1, 3] = np.nan\n",
    "df3.iloc[2, 2] = np.nan\n",
    "print(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd4ea5e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             A   B     C     D\n",
      "2025-01-01   0   1   2.0   3.0\n",
      "2025-01-04  12  13  14.0  15.0\n",
      "2025-01-05  16  17  13.0  19.0\n",
      "2025-01-06  20  21  22.0  23.0\n"
     ]
    }
   ],
   "source": [
    "# pandas 处理丢失的数据\n",
    "df = df3.dropna(axis=0, how=\"any\")  # how = {\"any\", \"all\"}  any: 含有NaN， all：全是NaN\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8f0cf591",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                A      B      C      D\n",
      "2025-01-01  False  False  False  False\n",
      "2025-01-02  False  False  False   True\n",
      "2025-01-03  False  False   True  False\n",
      "2025-01-04  False  False  False  False\n",
      "2025-01-05  False  False  False  False\n",
      "2025-01-06  False  False  False  False\n",
      "------------------------------\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print(df3.isnull())\n",
    "print(\"-\" * 30)\n",
    "print(np.any(df3.isnull()) == True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "18e1c803",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Student ID  name   age  gender\n",
      "0         1100  Kelly   22  Female\n",
      "1         1101    Clo   21  Female\n",
      "2         1102  Tilly   22  Female\n",
      "3         1103   Tony   24    Male\n",
      "4         1104  David   20    Male\n",
      "5         1105  Catty   22  Female\n",
      "6         1106      M    3  Female\n",
      "7         1107      N   43    Male\n",
      "8         1108      A   13    Male\n",
      "9         1109      S   12    Male\n",
      "10        1110  David   33    Male\n",
      "11        1111     Dw    3  Female\n",
      "12        1112      Q   23    Male\n",
      "13        1113      W   21  Female\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"student.csv\")\n",
    "print(data)\n",
    "data.to_pickle(\"student.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ee1bb3a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = pd.read_excel(\"C:\\\\Users\\\\25214\\\\Desktop\\\\抽卡记录.xlsx\")\n",
    "# display(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c3d90d81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.iloc[8, :] = [\"铃纱\", 1, 0, 0, np.nan]\n",
    "# data.to_excel(\"C:\\\\Users\\\\25214\\\\Desktop\\\\抽卡记录.xlsx\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dc6df08e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = pd.read_excel(\"C:\\\\Users\\\\25214\\\\Desktop\\\\抽卡记录.xlsx\")\n",
    "# display(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "600e2b8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A\n",
      "0  1\n",
      "1  2\n",
      "2  3\n",
      "3  4\n"
     ]
    }
   ],
   "source": [
    "# pd.concat(objs, axis=0, join='outer', ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, sort=False)\n",
    "\"\"\"\n",
    "objs: 一个 list 或 dict，包含多个 DataFrame 或 Series，这些对象将被拼接。\n",
    "axis: 0 或 1。0 表示按行拼接（纵向拼接），1 表示按列拼接（横向拼接）。默认为 0。\n",
    "join: ‘outer’ 或 ‘inner’。指定如何处理合并时的索引对齐方式：\n",
    "‘outer’：取并集，默认行为，保留所有的索引（即使某些列/行不存在于某些 DataFrame 中）。\n",
    "‘inner’：取交集，只有在所有 DataFrame 中都有的列或行才会出现在结果中。\n",
    "ignore_index: bool，默认值为 False。如果为 True，则忽略原有的索引，并为新 DataFrame 分配新的整数索引。\n",
    "keys: list，默认为 None。如果传递了 keys，会将拼接的 DataFrame 按 keys 参数组合成层次化的索引。\n",
    "levels: 用于指定 keys 的层级信息。通常配合 keys 使用。\n",
    "names: 用于设置多层索引的名字。\n",
    "verify_integrity: bool，默认值为 False。如果为 True，检查拼接后的数据是否存在重复的索引。\n",
    "sort: bool，默认值为 False。如果为 True，则会对列进行排序。\n",
    "\"\"\"\n",
    "df1 = pd.DataFrame({'A': [1, 2]}, index=[0, 1])\n",
    "df2 = pd.DataFrame({'A': [3, 4]}, index=[3, 4])\n",
    "\n",
    "# 使用 ignore_index=True，重置索引\n",
    "result = pd.concat([df1, df2], ignore_index=True)\n",
    "\n",
    "print(result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8ae61a92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ID     Name         City\n",
      "0   3  Charlie     New York\n",
      "1   4    David  Los Angeles\n"
     ]
    }
   ],
   "source": [
    "# pandas.merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True)\n",
    "\"\"\"\n",
    "left: 第一个 DataFrame，即左侧的 DataFrame。\n",
    "right: 第二个 DataFrame，即右侧的 DataFrame。\n",
    "how: 连接方式，支持以下几种方式：\n",
    "'inner'（默认）：交集，只保留两个 DataFrame 中都有的部分。\n",
    "'outer'：并集，保留两个 DataFrame 中的所有数据，缺失部分填充 NaN。\n",
    "'left'：左连接，保留左侧 DataFrame 的所有数据，右侧匹配不到的部分填充 NaN。\n",
    "'right'：右连接，保留右侧 DataFrame 的所有数据，左侧匹配不到的部分填充 NaN。\n",
    "on: 要连接的列名（如果两个 DataFrame 具有相同的列名）。如果两个 DataFrame 中连接列的名称不同，使用 left_on 和 right_on。\n",
    "left_on: 左侧 DataFrame 用来连接的列名（如果列名不同）。\n",
    "right_on: 右侧 DataFrame 用来连接的列名（如果列名不同）。\n",
    "left_index: bool，是否使用左侧 DataFrame 的索引作为连接键。默认 False。\n",
    "right_index: bool，是否使用右侧 DataFrame 的索引作为连接键。默认 False。\n",
    "sort: bool，是否对合并后的结果按连接键进行排序，默认为 False。\n",
    "suffixes: 用于为连接的列名称添加后缀，避免列名重复，默认值 ('_x', '_y')。\n",
    "\"\"\"\n",
    "import pandas as pd\n",
    "\n",
    "# 创建两个 DataFrame\n",
    "df1 = pd.DataFrame({\n",
    "    'ID': [1, 2, 3, 4],\n",
    "    'Name': ['Alice', 'Bob', 'Charlie', 'David']\n",
    "})\n",
    "\n",
    "df2 = pd.DataFrame({\n",
    "    'ID': [3, 4, 5, 6],\n",
    "    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston']\n",
    "})\n",
    "\n",
    "# 使用 merge 进行内连接\n",
    "result = pd.merge(df1, df2, how='inner', on='ID')\n",
    "\n",
    "print(result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "63eff33f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "<bound method NDFrame.head of              A          B          C          D\n",
      "0    -1.713592   0.954413  -1.421680   0.085018\n",
      "1    -1.576060   1.460772  -1.778044   1.195120\n",
      "2    -2.789289   0.551481  -2.292242   1.859775\n",
      "3    -3.002192  -0.407748  -3.806172   0.854935\n",
      "4    -4.552647   1.032498  -3.032712   1.963966\n",
      "..         ...        ...        ...        ...\n",
      "995 -15.897350 -40.566877 -30.242477 -42.830572\n",
      "996 -15.512032 -38.459944 -28.962802 -43.329948\n",
      "997 -14.510200 -39.285961 -27.118751 -43.717783\n",
      "998 -14.118257 -38.219697 -25.869038 -44.034478\n",
      "999 -13.297163 -36.019895 -24.207943 -44.191295\n",
      "\n",
      "[1000 rows x 4 columns]>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "data = pd.Series(np.random.randn(1000), index=np.arange(1000))\n",
    "data = data.cumsum()\n",
    "data.plot()\n",
    "\n",
    "print(\"-\" * 40)\n",
    "\n",
    "data = pd.DataFrame(np.random.randn(1000, 4),\n",
    "                   index=np.arange(1000),\n",
    "                   columns=list(\"ABCD\"))\n",
    "data = data.cumsum()\n",
    "print(data.head)\n",
    "data.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11b2f1d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel(\"C:\\\\Users\\\\25214\\\\Desktop\\\\2025年全国各专业招生计划.xlsx\")\n",
    "display(data)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82bb9a32",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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